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mod.rs
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use std::fmt;
use std::ops::ControlFlow;
use charabia::normalizer::NormalizerOption;
use charabia::Normalize;
use fst::automaton::{Automaton, Str};
use fst::{IntoStreamer, Streamer};
use levenshtein_automata::{LevenshteinAutomatonBuilder as LevBuilder, DFA};
use log::error;
use once_cell::sync::Lazy;
use roaring::bitmap::RoaringBitmap;
pub use self::facet::{FacetDistribution, Filter, OrderBy, DEFAULT_VALUES_PER_FACET};
pub use self::new::matches::{FormatOptions, MatchBounds, Matcher, MatcherBuilder, MatchingWords};
use self::new::PartialSearchResult;
use crate::error::UserError;
use crate::heed_codec::facet::{FacetGroupKey, FacetGroupValue};
use crate::score_details::{ScoreDetails, ScoringStrategy};
use crate::{
execute_search, AscDesc, DefaultSearchLogger, DocumentId, FieldId, Index, Result,
SearchContext, BEU16,
};
// Building these factories is not free.
static LEVDIST0: Lazy<LevBuilder> = Lazy::new(|| LevBuilder::new(0, true));
static LEVDIST1: Lazy<LevBuilder> = Lazy::new(|| LevBuilder::new(1, true));
static LEVDIST2: Lazy<LevBuilder> = Lazy::new(|| LevBuilder::new(2, true));
/// The maximum number of facets returned by the facet search route.
const MAX_NUMBER_OF_FACETS: usize = 100;
pub mod facet;
mod fst_utils;
pub mod new;
pub struct Search<'a> {
query: Option<String>,
vector: Option<Vec<f32>>,
// this should be linked to the String in the query
filter: Option<Filter<'a>>,
offset: usize,
limit: usize,
sort_criteria: Option<Vec<AscDesc>>,
searchable_attributes: Option<&'a [String]>,
geo_strategy: new::GeoSortStrategy,
terms_matching_strategy: TermsMatchingStrategy,
scoring_strategy: ScoringStrategy,
words_limit: usize,
exhaustive_number_hits: bool,
rtxn: &'a heed::RoTxn<'a>,
index: &'a Index,
}
impl<'a> Search<'a> {
pub fn new(rtxn: &'a heed::RoTxn, index: &'a Index) -> Search<'a> {
Search {
query: None,
vector: None,
filter: None,
offset: 0,
limit: 20,
sort_criteria: None,
searchable_attributes: None,
geo_strategy: new::GeoSortStrategy::default(),
terms_matching_strategy: TermsMatchingStrategy::default(),
scoring_strategy: Default::default(),
exhaustive_number_hits: false,
words_limit: 10,
rtxn,
index,
}
}
pub fn query(&mut self, query: impl Into<String>) -> &mut Search<'a> {
self.query = Some(query.into());
self
}
pub fn vector(&mut self, vector: impl Into<Vec<f32>>) -> &mut Search<'a> {
self.vector = Some(vector.into());
self
}
pub fn offset(&mut self, offset: usize) -> &mut Search<'a> {
self.offset = offset;
self
}
pub fn limit(&mut self, limit: usize) -> &mut Search<'a> {
self.limit = limit;
self
}
pub fn sort_criteria(&mut self, criteria: Vec<AscDesc>) -> &mut Search<'a> {
self.sort_criteria = Some(criteria);
self
}
pub fn searchable_attributes(&mut self, searchable: &'a [String]) -> &mut Search<'a> {
self.searchable_attributes = Some(searchable);
self
}
pub fn terms_matching_strategy(&mut self, value: TermsMatchingStrategy) -> &mut Search<'a> {
self.terms_matching_strategy = value;
self
}
pub fn scoring_strategy(&mut self, value: ScoringStrategy) -> &mut Search<'a> {
self.scoring_strategy = value;
self
}
pub fn words_limit(&mut self, value: usize) -> &mut Search<'a> {
self.words_limit = value;
self
}
pub fn filter(&mut self, condition: Filter<'a>) -> &mut Search<'a> {
self.filter = Some(condition);
self
}
#[cfg(test)]
pub fn geo_sort_strategy(&mut self, strategy: new::GeoSortStrategy) -> &mut Search<'a> {
self.geo_strategy = strategy;
self
}
/// Forces the search to exhaustively compute the number of candidates,
/// this will increase the search time but allows finite pagination.
pub fn exhaustive_number_hits(&mut self, exhaustive_number_hits: bool) -> &mut Search<'a> {
self.exhaustive_number_hits = exhaustive_number_hits;
self
}
pub fn execute(&self) -> Result<SearchResult> {
let mut ctx = SearchContext::new(self.index, self.rtxn);
if let Some(searchable_attributes) = self.searchable_attributes {
ctx.searchable_attributes(searchable_attributes)?;
}
let PartialSearchResult { located_query_terms, candidates, documents_ids, document_scores } =
execute_search(
&mut ctx,
&self.query,
&self.vector,
self.terms_matching_strategy,
self.scoring_strategy,
self.exhaustive_number_hits,
&self.filter,
&self.sort_criteria,
self.geo_strategy,
self.offset,
self.limit,
Some(self.words_limit),
&mut DefaultSearchLogger,
&mut DefaultSearchLogger,
)?;
// consume context and located_query_terms to build MatchingWords.
let matching_words = match located_query_terms {
Some(located_query_terms) => MatchingWords::new(ctx, located_query_terms),
None => MatchingWords::default(),
};
Ok(SearchResult { matching_words, candidates, document_scores, documents_ids })
}
}
impl fmt::Debug for Search<'_> {
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
let Search {
query,
vector: _,
filter,
offset,
limit,
sort_criteria,
searchable_attributes,
geo_strategy: _,
terms_matching_strategy,
scoring_strategy,
words_limit,
exhaustive_number_hits,
rtxn: _,
index: _,
} = self;
f.debug_struct("Search")
.field("query", query)
.field("vector", &"[...]")
.field("filter", filter)
.field("offset", offset)
.field("limit", limit)
.field("sort_criteria", sort_criteria)
.field("searchable_attributes", searchable_attributes)
.field("terms_matching_strategy", terms_matching_strategy)
.field("scoring_strategy", scoring_strategy)
.field("exhaustive_number_hits", exhaustive_number_hits)
.field("words_limit", words_limit)
.finish()
}
}
#[derive(Default, Debug)]
pub struct SearchResult {
pub matching_words: MatchingWords,
pub candidates: RoaringBitmap,
pub documents_ids: Vec<DocumentId>,
pub document_scores: Vec<Vec<ScoreDetails>>,
}
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum TermsMatchingStrategy {
// remove last word first
Last,
// all words are mandatory
All,
}
impl Default for TermsMatchingStrategy {
fn default() -> Self {
Self::Last
}
}
fn get_first(s: &str) -> &str {
match s.chars().next() {
Some(c) => &s[..c.len_utf8()],
None => panic!("unexpected empty query"),
}
}
pub fn build_dfa(word: &str, typos: u8, is_prefix: bool) -> DFA {
let lev = match typos {
0 => &LEVDIST0,
1 => &LEVDIST1,
_ => &LEVDIST2,
};
if is_prefix {
lev.build_prefix_dfa(word)
} else {
lev.build_dfa(word)
}
}
pub struct SearchForFacetValues<'a> {
query: Option<String>,
facet: String,
search_query: Search<'a>,
}
impl<'a> SearchForFacetValues<'a> {
pub fn new(facet: String, search_query: Search<'a>) -> SearchForFacetValues<'a> {
SearchForFacetValues { query: None, facet, search_query }
}
pub fn query(&mut self, query: impl Into<String>) -> &mut Self {
self.query = Some(query.into());
self
}
fn one_original_value_of(
&self,
field_id: FieldId,
facet_str: &str,
any_docid: DocumentId,
) -> Result<Option<String>> {
let index = self.search_query.index;
let rtxn = self.search_query.rtxn;
let key: (FieldId, _, &str) = (field_id, any_docid, facet_str);
Ok(index.field_id_docid_facet_strings.get(rtxn, &key)?.map(|v| v.to_owned()))
}
pub fn execute(&self) -> Result<Vec<FacetValueHit>> {
let index = self.search_query.index;
let rtxn = self.search_query.rtxn;
let filterable_fields = index.filterable_fields(rtxn)?;
if !filterable_fields.contains(&self.facet) {
let (valid_fields, hidden_fields) =
index.remove_hidden_fields(rtxn, filterable_fields)?;
return Err(UserError::InvalidFacetSearchFacetName {
field: self.facet.clone(),
valid_fields,
hidden_fields,
}
.into());
}
let fields_ids_map = index.fields_ids_map(rtxn)?;
let fid = match fields_ids_map.id(&self.facet) {
Some(fid) => fid,
// we return an empty list of results when the attribute has been
// set as filterable but no document contains this field (yet).
None => return Ok(Vec::new()),
};
let fst = match self.search_query.index.facet_id_string_fst.get(rtxn, &BEU16::new(fid))? {
Some(fst) => fst,
None => return Ok(vec![]),
};
let search_candidates = self.search_query.execute()?.candidates;
match self.query.as_ref() {
Some(query) => {
let options = NormalizerOption { lossy: true, ..Default::default() };
let query = query.normalize(&options);
let query = query.as_ref();
let authorize_typos = self.search_query.index.authorize_typos(rtxn)?;
let field_authorizes_typos =
!self.search_query.index.exact_attributes_ids(rtxn)?.contains(&fid);
if authorize_typos && field_authorizes_typos {
let exact_words_fst = self.search_query.index.exact_words(rtxn)?;
if exact_words_fst.map_or(false, |fst| fst.contains(query)) {
let mut results = vec![];
if fst.contains(query) {
self.fetch_original_facets_using_normalized(
fid,
query,
query,
&search_candidates,
&mut results,
)?;
}
Ok(results)
} else {
let one_typo = self.search_query.index.min_word_len_one_typo(rtxn)?;
let two_typos = self.search_query.index.min_word_len_two_typos(rtxn)?;
let is_prefix = true;
let automaton = if query.len() < one_typo as usize {
build_dfa(query, 0, is_prefix)
} else if query.len() < two_typos as usize {
build_dfa(query, 1, is_prefix)
} else {
build_dfa(query, 2, is_prefix)
};
let mut stream = fst.search(automaton).into_stream();
let mut results = vec![];
while let Some(facet_value) = stream.next() {
let value = std::str::from_utf8(facet_value)?;
if self
.fetch_original_facets_using_normalized(
fid,
value,
query,
&search_candidates,
&mut results,
)?
.is_break()
{
break;
}
}
Ok(results)
}
} else {
let automaton = Str::new(query).starts_with();
let mut stream = fst.search(automaton).into_stream();
let mut results = vec![];
while let Some(facet_value) = stream.next() {
let value = std::str::from_utf8(facet_value)?;
if self
.fetch_original_facets_using_normalized(
fid,
value,
query,
&search_candidates,
&mut results,
)?
.is_break()
{
break;
}
}
Ok(results)
}
}
None => {
let mut results = vec![];
let prefix = FacetGroupKey { field_id: fid, level: 0, left_bound: "" };
for result in index.facet_id_string_docids.prefix_iter(rtxn, &prefix)? {
let (FacetGroupKey { left_bound, .. }, FacetGroupValue { bitmap, .. }) =
result?;
let count = search_candidates.intersection_len(&bitmap);
if count != 0 {
let value = self
.one_original_value_of(fid, left_bound, bitmap.min().unwrap())?
.unwrap_or_else(|| left_bound.to_string());
results.push(FacetValueHit { value, count });
}
if results.len() >= MAX_NUMBER_OF_FACETS {
break;
}
}
Ok(results)
}
}
}
fn fetch_original_facets_using_normalized(
&self,
fid: FieldId,
value: &str,
query: &str,
search_candidates: &RoaringBitmap,
results: &mut Vec<FacetValueHit>,
) -> Result<ControlFlow<()>> {
let index = self.search_query.index;
let rtxn = self.search_query.rtxn;
let database = index.facet_id_normalized_string_strings;
let key = (fid, value);
let original_strings = match database.get(rtxn, &key)? {
Some(original_strings) => original_strings,
None => {
error!("the facet value is missing from the facet database: {key:?}");
return Ok(ControlFlow::Continue(()));
}
};
for original in original_strings {
let key = FacetGroupKey { field_id: fid, level: 0, left_bound: original.as_str() };
let docids = match index.facet_id_string_docids.get(rtxn, &key)? {
Some(FacetGroupValue { bitmap, .. }) => bitmap,
None => {
error!("the facet value is missing from the facet database: {key:?}");
return Ok(ControlFlow::Continue(()));
}
};
let count = search_candidates.intersection_len(&docids);
if count != 0 {
let value = self
.one_original_value_of(fid, &original, docids.min().unwrap())?
.unwrap_or_else(|| query.to_string());
results.push(FacetValueHit { value, count });
}
if results.len() >= MAX_NUMBER_OF_FACETS {
return Ok(ControlFlow::Break(()));
}
}
Ok(ControlFlow::Continue(()))
}
}
#[derive(Debug, Clone, serde::Serialize, PartialEq)]
pub struct FacetValueHit {
/// The original facet value
pub value: String,
/// The number of documents associated to this facet
pub count: u64,
}
#[cfg(test)]
mod test {
#[allow(unused_imports)]
use super::*;
#[cfg(feature = "japanese")]
#[test]
fn test_kanji_language_detection() {
use crate::index::tests::TempIndex;
let index = TempIndex::new();
index
.add_documents(documents!([
{ "id": 0, "title": "The quick (\"brown\") fox can't jump 32.3 feet, right? Brr, it's 29.3°F!" },
{ "id": 1, "title": "東京のお寿司。" },
{ "id": 2, "title": "הַשּׁוּעָל הַמָּהִיר (״הַחוּם״) לֹא יָכוֹל לִקְפֹּץ 9.94 מֶטְרִים, נָכוֹן? ברר, 1.5°C- בַּחוּץ!" }
]))
.unwrap();
let txn = index.write_txn().unwrap();
let mut search = Search::new(&txn, &index);
search.query("東京");
let SearchResult { documents_ids, .. } = search.execute().unwrap();
assert_eq!(documents_ids, vec![1]);
}
}